






"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE   :							Dict          =		logging.get_logger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Dict		=					["""pixel_values"""]






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Optional[Dict[str, int]] = None			,		__SCREAMING_SNAKE_CASE	:				PILImageResampling = PILImageResampling.BILINEAR			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Dict[str, int] = None			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Union[int, float] = 1 / 255			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[float, List[float]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[float, List[float]]] = None			,		**__SCREAMING_SNAKE_CASE	:				str			,		)	->						None:
      super().__init__(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		size if size is not None else {'shortest_edge': 256}
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE			,		default_to_square=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		crop_size if crop_size is not None else {'height': 224, 'width': 224}
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		do_resize
      lowerCamelCase_       	=		size
      lowerCamelCase_       	=		resample
      lowerCamelCase_       	=		do_center_crop
      lowerCamelCase_       	=		crop_size
      lowerCamelCase_       	=		do_rescale
      lowerCamelCase_       	=		rescale_factor
      lowerCamelCase_       	=		do_normalize
      lowerCamelCase_       	=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      lowerCamelCase_       	=		image_std if image_std is not None else IMAGENET_STANDARD_STD






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				np.ndarray			,		__SCREAMING_SNAKE_CASE	:				Dict[str, int]			,		__SCREAMING_SNAKE_CASE	:				PILImageResampling = PILImageResampling.BICUBIC			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, ChannelDimension]] = None			,		**__SCREAMING_SNAKE_CASE	:				int			,		)	->						np.ndarray:
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE			,		default_to_square=__SCREAMING_SNAKE_CASE   )
      if "shortest_edge" not in size:
        raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
      lowerCamelCase_       	=		get_resize_output_image_size(__SCREAMING_SNAKE_CASE			,		size=size['shortest_edge']			,		default_to_square=__SCREAMING_SNAKE_CASE   )
      return resize(__SCREAMING_SNAKE_CASE			,		size=__SCREAMING_SNAKE_CASE			,		resample=__SCREAMING_SNAKE_CASE			,		data_format=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				np.ndarray			,		__SCREAMING_SNAKE_CASE	:				Dict[str, int]			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, ChannelDimension]] = None			,		**__SCREAMING_SNAKE_CASE	:				List[str]			,		)	->						np.ndarray:
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE   )
      return center_crop(__SCREAMING_SNAKE_CASE			,		size=(size['height'], size['width'])			,		data_format=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				np.ndarray			,		__SCREAMING_SNAKE_CASE	:				float			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, ChannelDimension]] = None			,		**__SCREAMING_SNAKE_CASE	:				int   )	->						np.ndarray:
      return rescale(__SCREAMING_SNAKE_CASE			,		scale=__SCREAMING_SNAKE_CASE			,		data_format=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				np.ndarray			,		__SCREAMING_SNAKE_CASE	:				Union[float, List[float]]			,		__SCREAMING_SNAKE_CASE	:				Union[float, List[float]]			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, ChannelDimension]] = None			,		**__SCREAMING_SNAKE_CASE	:				Dict			,		)	->						np.ndarray:
      return normalize(__SCREAMING_SNAKE_CASE			,		mean=__SCREAMING_SNAKE_CASE			,		std=__SCREAMING_SNAKE_CASE			,		data_format=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				ImageInput			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = None			,		__SCREAMING_SNAKE_CASE	:				Dict[str, int] = None			,		__SCREAMING_SNAKE_CASE	:				PILImageResampling = None			,		__SCREAMING_SNAKE_CASE	:				bool = None			,		__SCREAMING_SNAKE_CASE	:				Dict[str, int] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[float] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[bool] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[float, List[float]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[float, List[float]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, TensorType]] = None			,		__SCREAMING_SNAKE_CASE	:				Union[str, ChannelDimension] = ChannelDimension.FIRST			,		**__SCREAMING_SNAKE_CASE	:				Any			,		)	->						List[str]:
      lowerCamelCase_       	=		do_resize if do_resize is not None else self.do_resize
      lowerCamelCase_       	=		size if size is not None else self.size
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE			,		default_to_square=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		resample if resample is not None else self.resample
      lowerCamelCase_       	=		do_center_crop if do_center_crop is not None else self.do_center_crop
      lowerCamelCase_       	=		crop_size if crop_size is not None else self.crop_size
      lowerCamelCase_       	=		get_size_dict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		do_rescale if do_rescale is not None else self.do_rescale
      lowerCamelCase_       	=		rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCamelCase_       	=		do_normalize if do_normalize is not None else self.do_normalize
      lowerCamelCase_       	=		image_mean if image_mean is not None else self.image_mean
      lowerCamelCase_       	=		image_std if image_std is not None else self.image_std

      lowerCamelCase_       	=		make_list_of_images(__SCREAMING_SNAKE_CASE   )

      if not valid_images(__SCREAMING_SNAKE_CASE   ):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.'   )

      if do_resize and size is None:
        raise ValueError('Size must be specified if do_resize is True.'   )

      if do_center_crop and crop_size is None:
        raise ValueError('Crop size must be specified if do_center_crop is True.'   )

      if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

      if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

      # All transformations expect numpy arrays.
      lowerCamelCase_       	=		[to_numpy_array(__SCREAMING_SNAKE_CASE   ) for image in images]

      if do_resize:
        lowerCamelCase_       	=		[self.resize(image=__SCREAMING_SNAKE_CASE			,		size=__SCREAMING_SNAKE_CASE			,		resample=__SCREAMING_SNAKE_CASE   ) for image in images]

      if do_center_crop:
        lowerCamelCase_       	=		[self.center_crop(image=__SCREAMING_SNAKE_CASE			,		size=__SCREAMING_SNAKE_CASE   ) for image in images]

      if do_rescale:
        lowerCamelCase_       	=		[self.rescale(image=__SCREAMING_SNAKE_CASE			,		scale=__SCREAMING_SNAKE_CASE   ) for image in images]

      if do_normalize:
        lowerCamelCase_       	=		[self.normalize(image=__SCREAMING_SNAKE_CASE			,		mean=__SCREAMING_SNAKE_CASE			,		std=__SCREAMING_SNAKE_CASE   ) for image in images]

      lowerCamelCase_       	=		[to_channel_dimension_format(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) for image in images]

      lowerCamelCase_       	=		{'pixel_values': images}
      return BatchFeature(data=__SCREAMING_SNAKE_CASE			,		tensor_type=__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




